The invention relates to a method for visualising a chronological sequence of measurements, in particular obtained from a continuous glucose monitoring process. The invention further relates to a device for processing and visualising such a chronological sequence of measurements.
Monitoring parameters measured in or on the body of humans such as a concentration of a certain substance in a given body fluid has many applications. In particular, the monitoring is crucial in the context of therapies that involve an administration of active substances regulated depending on the value of one or a plurality of physiological parameters. A prominent example is diabetes therapy where the administration of insulin is effected depending on a measured glucose concentration in a body fluid of the patient.
Conventionally, diabetic patients who need to regularly administer insulin have periodically taken measurements of their blood glucose level, e.g. using a hand held strip-based glucose meter. However, the small number of measurements (usually four a day) provide only a very coarse picture of the progression of the glucose level (“snapshots” in time). They cannot give dynamic information about the metabolic response to a specific event such as a meal or physical activities of the patient; or more generally, the glucose trend during a period of time.
Continuous glucose monitoring (CGM) is a new technology for diabetes self-management. Instruments for continuous glucose monitoring record glucose concentrations over a period of time that lasts from several hours to several days, weeks or even months. The measurement frequency is much higher than that of the traditional spot blood glucose (bG) measurements referred to above, namely usually at least 10 measurements per hour. In principle, the increased temporal resolution provides the patient as well as his or her health care provider(s) (HCP) with a rich data set of time-variant glucose information. In principle, the continually measured glucose data can be used to more specifically adjust and refine the diabetes therapy to individual needs by adjusting the basal insulin rate as well as the timing and the amount of boluses. Furthermore, the data provides indications about advisable changes of the patient's behaviour, e.g. concerning different food choices (type, portion) or activity changes.
However, the advantages of CGM can easily be overwhelmed by the drawbacks of too much information. As CGM is a data-intensive method, visualising measured data in an intuitive way is crucial in order to allow easy interpretation of the measured data. The common way of visualising the measured data is by plotting curves in a Cartesian coordinate graphing system. In such a Cartesian coordinate graphing system one of the two axes (e.g. the horizontal x-axis) represents the progress in the measurement pattern, in particular this axis represents time. The other axis (e. g. the vertical y-axis) then represents the measured values, in particular the measured glucose level for each measurement. As time proceeds, an interpolated curve can be plotted from the measured data.
Multiple data sets of continuous glucose information, e.g. corresponding to different time periods such as three data sets of the same patient taken on three different days, can be graphically overlaid for the purpose of comparison and pattern recognition. However, comparing such overlaid curves is difficult due to the complexity of the corresponding graphical representation. Furthermore, physicians (or other health care providers carrying responsibility for a patient's therapy) are primarily interested in a few key aspects of diabetic health that may not be immediately apparent in the visual confusion of a complex Cartesian line graph. In the case of diabetes therapy, among these key aspects are: The relative time spent in hyper- versus hypoglycemia; the intensity of hyper- and hypoglycemic events; and tendencies for a patient to have glucose excursions during certain times of day, or certain days of the week.
Moreover, the average patient is usually unable to interpret the measurements being performed at his/her body. As recognition and attribution of certain shapes in the presented data to certain events during the measurement pattern plays a key role in a successful diagnosis process, improving the visualisation of the measured data is important. To recognise important aspects of a measurement pattern such as e.g. the relative time spent in hyper- and hypoglycemia and the intensity of hyper- and hypoglycemic events a patient or a physician needs further analysis of the curve(s) shown in a Cartesian coordinate graphing system.